Motivation

Image source: https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970

The replication crisis (also called the replicability crisis and the reproducibility crisis) is an ongoing methodological crisis in which the results of many scientific studies are difficult or impossible to reproduce
Reproducible crisis lead to
- Low quality of medical research
- low quality code and contain errors
- Reproducibility is frequently laborious and time-consuming.

Raw data to summary table

SPSS output to summary table

R output to summary table

Thus, gtsummary package were developed to help the non-coder R users to produce a presentation ready table that are reproducible and customizable.

Image source: Happy R adapted from artwork by @allison_horst; the beach and cocktail images are from pngtree.com

Introduction

Overview of {gtsummary} package

The {gtsummary} package provides an elegant and flexible way to create publication-ready analytical and summary tables using the R programming language.
A package developed by Daniel D.Sjoberg et al.
Use gt package as a background to produced a highly reproducible and presentation ready table.
Latest version: 1.7.2 (2023-07-15)
Requirement: R ≥ 3.4

Import Packages

broom (>= 0.8.0)	broom.helpers (>= 1.9.0)	cli (>= 3.1.1)
dplyr (>= 1.0.7)	forcats (>= 0.5.1)	glue (>= 1.6.0)
gt (>= 0.7.0)	knitr (>= 1.37)	lifecycle (>= 1.0.1)
purrr (>= 0.3.4)	rlang (>= 1.0.3)	stringr (>= 1.4.0)
tibble (>= 3.1.6)	tidyr (>= 1.1.4)

Function

Creates default tabular summaries with highly customizable capabilities
Summarize data frames (survival data, survey data)
Cross-tabulation
Summarize regression models (linear, logistics and survival)
Report statistics from gtsummary tables in-line in Rmarkdown
Stack and /or merge any table type
Standardize themes across tables
Choose different print engines

Analysis Workflow

Function	Customization			Print engines	Theme
gtsummary function	Data arrangement	Additional information	Table cosmetics	coding	coding
tbl_summary	by:	add_*	modify_*	as_gt	reset_gtsummary_theme
tbl_cross	type:		bold_*	as_flex_table	theme_gtsummary_journal(journal = “lancet”) can choose “lancet”,“jama” and others
tbl_uvregression	statistics:		italicize_*	as_hux_table
tbl_regression	lable:			as_kable_extra
tbl_merge				as_kable
tbl_stack				as_tibble

library(haven)
stroke <- read.csv("rconf.csv", stringsAsFactors = TRUE)

stroke$age <- as.numeric(stroke$age)

summary(stroke)

##       age            sex        ethnicity      married       dm        hpt     
##  Min.   :24.00   Female:123   Chinese: 34   Divorce:  8   No  :170   No  : 69  
##  1st Qu.:54.00   Male  :196   Indian : 25   Married:168   Yes :137   Yes :242  
##  Median :63.00                Malay  :259   Single :  6   NA's: 12   NA's:  8  
##  Mean   :63.12                Others :  1   NA's   :137                        
##  3rd Qu.:73.00                                                                 
##  Max.   :95.00                                                                 
##  NA's   :8                                                                     
##    ckd         af       hf.IHD     lipid      smoke    
##      :129       : 14       :129   No  :221   No  :285  
##  No  :189   No  :292   No  :175   Yes : 85   Yes : 29  
##  Yes :  1   Yes : 13   Yes : 15   NA's: 13   NA's:  5  
##                                                        
##                                                        
##                                                        
##                                                        
##                              WHO             dodiag         gcs     
##  ICH                           : 14   Missing   : 26   15     :243  
##  Intracerebral Hemorrhage (ICH): 21   15/03/2021:  6   10     : 15  
##  Ischaemic                     :281   01/03/2021:  4   13     : 12  
##  SAH                           :  3   03/01/2021:  4   11     : 11  
##                                       03/03/2021:  4   12     : 10  
##                                       04/01/2021:  4   9      :  9  
##                                       (Other)   :271   (Other): 19  
##                                nihss                               mrs    
##  Minor stroke (1-4)               :130   Moderately severe disability:85  
##  Moderate stroke (5-15)           : 86   No significant disability   :60  
##  Moderate to severe stroke (16-20): 20   Slight disability           :53  
##  No stroke symptoms (0)           : 45   Severe disability           :37  
##  Severe stroke (21-42)            : 17   Moderate disability         :35  
##  NA's                             : 21   (Other)                     :23  
##                                          NA's                        :26  
##  iv_thrombolysis iv_thrombectomy status_dis         dodis     status_f.u 
##  No :315         No:319          Alive:306             :129   Alive:228  
##  Yes:  4                         Death: 13   25/01/2018:  4   Died : 91  
##                                              02/03/2021:  3              
##                                              07/10/2020:  3              
##                                              08/01/2018:  3              
##                                              10/01/2018:  3              
##                                              (Other)   :174              
##        dodeath         Sebab.Kematian
##  22/11/2022:228               :228   
##  01/06/2021:  2   SAKIT TUA   : 36   
##  08/02/2021:  2   STROK       :  5   
##  08/04/2022:  2   SAKIT STROK :  4   
##  12/03/2021:  2   STROKE      :  4   
##  12/06/2022:  2   DARAH TINGGI:  2   
##  (Other)   : 81   (Other)     : 40

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(gtsummary)

stroke$dodiag <- as.Date(stroke$dodiag)
stroke$dodeath <- as.Date(stroke$dodeath)
stroke$dodis <- as.Date(stroke$dodis)
stroke <- stroke %>% mutate(dur = stroke$dodiag %--% stroke$dodeath) %>% 
  mutate(dur = as.duration(dur))

stroke <- stroke %>% mutate(dur_days = dur/ddays(1))

stroke <- stroke %>% mutate(dur_month = dur/ddays(1)/30.417)

str(stroke)

## 'data.frame':    319 obs. of  26 variables:
##  $ age            : num  52 73 64 58 64 64 66 52 76 45 ...
##  $ sex            : Factor w/ 2 levels "Female","Male ": 2 2 2 2 2 1 2 2 2 2 ...
##  $ ethnicity      : Factor w/ 4 levels "Chinese","Indian",..: 3 3 3 3 3 3 3 3 1 3 ...
##  $ married        : Factor w/ 3 levels "Divorce","Married",..: 2 2 2 2 2 2 1 2 2 2 ...
##  $ dm             : Factor w/ 2 levels "No","Yes ": 1 2 2 2 1 2 2 1 1 2 ...
##  $ hpt            : Factor w/ 2 levels "No","Yes ": 2 2 1 2 2 2 1 1 2 1 ...
##  $ ckd            : Factor w/ 3 levels "","No","Yes ": 2 2 2 2 2 2 2 2 2 2 ...
##  $ af             : Factor w/ 3 levels "","No","Yes ": 2 2 2 2 2 2 2 2 2 2 ...
##  $ hf.IHD         : Factor w/ 3 levels "","No","Yes ": 2 2 3 2 2 2 2 2 2 2 ...
##  $ lipid          : Factor w/ 2 levels "No","Yes": 1 1 1 2 2 1 1 1 1 1 ...
##  $ smoke          : Factor w/ 2 levels "No","Yes ": 1 1 1 1 1 1 1 1 1 1 ...
##  $ WHO            : Factor w/ 4 levels "ICH","Intracerebral Hemorrhage (ICH)",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ dodiag         : Date, format: "0012-09-20" "0022-09-20" ...
##  $ gcs            : Factor w/ 13 levels "10","11","12",..: 6 4 6 6 6 3 6 6 6 6 ...
##  $ nihss          : Factor w/ 5 levels "Minor stroke (1-4)",..: 2 2 2 2 1 1 1 2 1 1 ...
##  $ mrs            : Factor w/ 7 levels "Died (dischrage)",..: 3 7 6 3 4 7 7 3 7 4 ...
##  $ iv_thrombolysis: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ iv_thrombectomy: Factor w/ 1 level "No": 1 1 1 1 1 1 1 1 1 1 ...
##  $ status_dis     : Factor w/ 2 levels "Alive","Death": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dodis          : Date, format: "0018-09-20" "0024-09-20" ...
##  $ status_f.u     : Factor w/ 2 levels "Alive","Died": 1 1 2 1 1 2 1 1 1 1 ...
##  $ dodeath        : Date, format: "0022-11-20" "0022-11-20" ...
##  $ Sebab.Kematian : Factor w/ 44 levels "","ACUTE HEMORRHAGIC STROKE",..: 1 1 26 1 1 40 1 1 1 1 ...
##  $ dur            :Formal class 'Duration' [package "lubridate"] with 1 slot
##   .. ..@ .Data: num  3.21e+08 5.27e+06 -4.29e+08 5.65e+08 5.34e+08 ...
##  $ dur_days       : num  3713 61 -4962 6544 6179 ...
##  $ dur_month      : num  122.07 2.01 -163.13 215.14 203.14 ...

stroke2 <- stroke %>% select(age, sex, dm, hpt, nihss, status_f.u, dur_days,dur_month)
stroke2_complete <- na.omit(stroke2)
stroke2_complete$nihss <- relevel(stroke2_complete$nihss, ref = "No stroke symptoms (0)")

Application and examples

Descriptive analysis

tbl_summary()

library(gtsummary)
tbl_summary(stroke2_complete)

Characteristic	N = 266¹
age	64 (55, 73)
sex
Female	103 (39%)
Male	163 (61%)
dm
No	138 (52%)
Yes	128 (48%)
hpt
No	48 (18%)
Yes	218 (82%)
nihss
No stroke symptoms (0)	19 (7.1%)
Minor stroke (1-4)	126 (47%)
Moderate stroke (5-15)	85 (32%)
Moderate to severe stroke (16-20)	19 (7.1%)
Severe stroke (21-42)	17 (6.4%)
status_f.u
Alive	186 (70%)
Died	80 (30%)
dur_days	2,191 (-311, 5,075)
dur_month	72 (-10, 167)
¹ Median (IQR); n (%)

tbl_cross()

tbl_cross(stroke2_complete,
          row = sex,
          col = status_f.u,
          percent = "row",
          margin = "row") %>% 
  add_p(source_note = TRUE)

	status_f.u
	Alive	Died
sex
Female	62 (60%)	41 (40%)
Male	124 (76%)	39 (24%)
Total	186 (70%)	80 (30%)
Pearson’s Chi-squared test, p=0.006

Binary logistic regression

Default output for regression analysis in R

default_stroke <- glm(status_f.u~age + sex, data = stroke2_complete, family = binomial(link = logit))

summary(default_stroke)

## 
## Call:
## glm(formula = status_f.u ~ age + sex, family = binomial(link = logit), 
##     data = stroke2_complete)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -4.93939    0.89754  -5.503 3.73e-08 ***
## age          0.06785    0.01276   5.316 1.06e-07 ***
## sexMale     -0.64117    0.29368  -2.183    0.029 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 325.32  on 265  degrees of freedom
## Residual deviance: 283.23  on 263  degrees of freedom
## AIC: 289.23
## 
## Number of Fisher Scoring iterations: 4

Logistic regression using gtsummary-univariate analysis

uvlog <- stroke2_complete %>% select(age, sex, dm, hpt, nihss, status_f.u) %>% 
  tbl_uvregression(method = glm,
                   y=status_f.u,
                   method.args = list(family = binomial),
                   exponentiate = TRUE)

uvlog

Characteristic	N	OR¹	95% CI¹	p-value
age	266	1.07	1.05, 1.10	<0.001
sex	266
Female		—	—
Male		0.48	0.28, 0.81	0.006
dm	266
No		—	—
Yes		1.85	1.09, 3.16	0.024
hpt	266
No		—	—
Yes		1.56	0.77, 3.37	0.2
nihss	266
No stroke symptoms (0)		—	—
Minor stroke (1-4)		0.32	0.11, 1.00	0.040
Moderate stroke (5-15)		1.44	0.52, 4.45	0.5
Moderate to severe stroke (16-20)		2.98	0.81, 11.9	0.11
Severe stroke (21-42)		7.04	1.72, 34.6	0.010
¹ OR = Odds Ratio, CI = Confidence Interval

Logistic regression using gtsummary-multivariate analysis

mvlog <- glm(status_f.u~age+sex+dm+hpt+nihss,
             stroke2_complete, 
             family = binomial)
mvlog %>% tbl_regression(
  exponentiate=TRUE
)

Characteristic	OR¹	95% CI¹	p-value
age	1.07	1.04, 1.10	<0.001
sex
Female	—	—
Male	0.61	0.32, 1.16	0.13
dm
No	—	—
Yes	1.73	0.89, 3.41	0.11
hpt
No	—	—
Yes	0.74	0.29, 1.91	0.5
nihss
No stroke symptoms (0)	—	—
Minor stroke (1-4)	0.44	0.13, 1.55	0.2
Moderate stroke (5-15)	1.92	0.61, 6.62	0.3
Moderate to severe stroke (16-20)	4.54	1.03, 22.0	0.051
Severe stroke (21-42)	6.28	1.30, 35.8	0.028
¹ OR = Odds Ratio, CI = Confidence Interval

Survival analysis

Survival rate using tbl_survfit

library(survival)
library(gtsummary)
fit1 <- survfit(Surv(dur_month, status_f.u)~ 1, stroke2_complete)
fit2 <- survfit(Surv(dur_month, status_f.u)~ sex, stroke2_complete)

life_table <- list(fit1, fit2) %>% 
  tbl_survfit(times= c(1, 12, 36)) %>% 
  modify_header(update = list(
    stat_1 ~ "**1-month**",
    stat_2 ~ "**1-year**",
    stat_3 ~ "**3-years**"
  )) %>% 
  add_n()

## tbl_survfit: Multi-state model detected. Showing probabilities into state 'Died'
## tbl_survfit: Multi-state model detected. Showing probabilities into state 'Died'

life_table

Characteristic	N	1-month	1-year	3-years
Overall	266	0% (0%, 0%)	11% (8.0%, 16%)	14% (10%, 19%)
sex	266
Female		0% (0%, 0%)	16% (10%, 25%)	18% (12%, 28%)
Male		0% (0%, 0%)	8.4% (5.0%, 14%)	11% (6.8%, 17%)

Semi-parametric survival using gtsummary-univariate analysis

library(survival)
cox_uv <- tbl_uvregression(
  stroke2_complete[c("dur_month", "status_f.u", "age", "sex", "dm",
             "hpt", "nihss")],
  method = coxph,
  y = Surv(time = dur_month, event = status_f.u=='Died'),
  exponentiate = TRUE
)

cox_uv

Characteristic	N	HR¹	95% CI¹	p-value
age	266	1.05	1.03, 1.06	<0.001
sex	266
Female		—	—
Male		0.48	0.30, 0.74	0.001
dm	266
No		—	—
Yes		1.53	0.97, 2.39	0.066
hpt	266
No		—	—
Yes		1.32	0.70, 2.50	0.4
nihss	266
No stroke symptoms (0)		—	—
Minor stroke (1-4)		0.38	0.15, 0.99	0.047
Moderate stroke (5-15)		1.32	0.55, 3.16	0.5
Moderate to severe stroke (16-20)		1.66	0.61, 4.50	0.3
Severe stroke (21-42)		4.12	1.56, 10.9	0.004
¹ HR = Hazard Ratio, CI = Confidence Interval

Semi-parametric survival using gtsummary-multivariate analysis

cox_mv <- coxph(Surv(time = dur_month, event = status_f.u=='Died')~
                  age+sex+dm+hpt+nihss,
             stroke2_complete) %>% 
  tbl_regression(exponentiate=TRUE)
  
cox_mv

Characteristic	HR¹	95% CI¹	p-value
age	1.04	1.02, 1.06	<0.001
sex
Female	—	—
Male	0.60	0.38, 0.96	0.033
dm
No	—	—
Yes	1.88	1.14, 3.10	0.013
hpt
No	—	—
Yes	0.60	0.30, 1.18	0.14
nihss
No stroke symptoms (0)	—	—
Minor stroke (1-4)	0.68	0.25, 1.88	0.5
Moderate stroke (5-15)	2.20	0.88, 5.53	0.092
Moderate to severe stroke (16-20)	2.64	0.93, 7.48	0.067
Severe stroke (21-42)	7.40	2.56, 21.4	<0.001
¹ HR = Hazard Ratio, CI = Confidence Interval

Customization

Customization

{gtsummary} + formulas

Data arrangement

stroke3 <-  stroke2_complete %>% select(age, sex, dm, hpt, nihss)
desc <- tbl_summary(stroke3,
            by    = sex,
            label = list( age                 ~    "Age",
                          sex                 ~    "Gender",
                          dm                  ~    "Diabetes Mellitus",
                          hpt                 ~    "Hypertention",
                          nihss               ~    "NIHSS Score"),
            digits =      c(all_continuous()  ~    1,
                            all_categorical() ~    0),
            statistic =   c(all_categorical() ~    "{n} ({p}%)",
                            all_continuous()  ~    "{mean} ({sd})"))

desc

Characteristic	Female, N = 103¹	Male , N = 163¹
Age	65.1 (14.9)	62.9 (11.8)
Diabetes Mellitus
No	51 (50%)	87 (53%)
Yes	52 (50%)	76 (47%)
Hypertention
No	13 (13%)	35 (21%)
Yes	90 (87%)	128 (79%)
NIHSS Score
No stroke symptoms (0)	6 (6%)	13 (8%)
Minor stroke (1-4)	45 (44%)	81 (50%)
Moderate stroke (5-15)	32 (31%)	53 (33%)
Moderate to severe stroke (16-20)	9 (9%)	10 (6%)
Severe stroke (21-42)	11 (11%)	6 (4%)
¹ Mean (SD); n (%)

Add extra information

#for descriptive table
desc %>% 
  add_n %>% 
  add_p %>% 
  add_q

## add_q: Adjusting p-values with
## `stats::p.adjust(x$table_body$p.value, method = "fdr")`

Characteristic	N	Female, N = 103¹	Male , N = 163¹	p-value²	q-value³
Age	266	65.1 (14.9)	62.9 (11.8)	0.12	0.2
Diabetes Mellitus	266			0.5	0.5
No		51 (50%)	87 (53%)
Yes		52 (50%)	76 (47%)
Hypertention	266			0.067	0.2
No		13 (13%)	35 (21%)
Yes		90 (87%)	128 (79%)
NIHSS Score	266			0.2	0.2
No stroke symptoms (0)		6 (6%)	13 (8%)
Minor stroke (1-4)		45 (44%)	81 (50%)
Moderate stroke (5-15)		32 (31%)	53 (33%)
Moderate to severe stroke (16-20)		9 (9%)	10 (6%)
Severe stroke (21-42)		11 (11%)	6 (4%)
¹ Mean (SD); n (%)
² Wilcoxon rank sum test; Pearson’s Chi-squared test
³ False discovery rate correction for multiple testing

Aesthethic

desc %>% 
  add_n %>% 
  add_p %>% 
  add_q %>% 
  bold_labels() %>% 
  italicize_levels()

## add_q: Adjusting p-values with
## `stats::p.adjust(x$table_body$p.value, method = "fdr")`

Characteristic	N	Female, N = 103¹	Male , N = 163¹	p-value²	q-value³
Age	266	65.1 (14.9)	62.9 (11.8)	0.12	0.2
Diabetes Mellitus	266			0.5	0.5
No		51 (50%)	87 (53%)
Yes		52 (50%)	76 (47%)
Hypertention	266			0.067	0.2
No		13 (13%)	35 (21%)
Yes		90 (87%)	128 (79%)
NIHSS Score	266			0.2	0.2
No stroke symptoms (0)		6 (6%)	13 (8%)
Minor stroke (1-4)		45 (44%)	81 (50%)
Moderate stroke (5-15)		32 (31%)	53 (33%)
Moderate to severe stroke (16-20)		9 (9%)	10 (6%)
Severe stroke (21-42)		11 (11%)	6 (4%)
¹ Mean (SD); n (%)
² Wilcoxon rank sum test; Pearson’s Chi-squared test
³ False discovery rate correction for multiple testing

#may add bold_p if there is significant differenc with p value of <0.05

Merging and stacking

Stacking and merging

Merging

cox_uv <- tbl_uvregression(
  stroke2_complete[c("dur_month", "status_f.u", "age", "sex", "dm",
             "hpt", "nihss")],
  method = coxph,
  y = Surv(time = dur_month, event = status_f.u=='Died'),
  exponentiate = TRUE,
  label = list( age                 ~    "Age",
                          sex                 ~    "Gender",
                          dm                  ~    "Diabetes Mellitus",
                          hpt                 ~    "Hypertention",
                          nihss               ~    "NIHSS Score"),
)

cox_mv <- coxph(Surv(time = dur_month, event = status_f.u=='Died')~
                  age+sex+dm+hpt+nihss,
             stroke2_complete) %>% 
  tbl_regression(exponentiate=TRUE,
                 label = list( age                 ~    "Age",
                          sex                 ~    "Gender",
                          dm                  ~    "Diabetes Mellitus",
                          hpt                 ~    "Hypertention",
                          nihss               ~    "NIHSS Score"),)

tbl_surv_merge <- tbl_merge(
  list(cox_uv, cox_mv),
  tab_spanner = c("**Univariable**","**Multivariable**")
)
tbl_surv_merge

Characteristic	Univariable				Multivariable
Characteristic	N	HR¹	95% CI¹	p-value	HR¹	95% CI¹	p-value
Age	266	1.05	1.03, 1.06	<0.001	1.04	1.02, 1.06	<0.001
Gender	266
Female		—	—		—	—
Male		0.48	0.30, 0.74	0.001	0.60	0.38, 0.96	0.033
Diabetes Mellitus	266
No		—	—		—	—
Yes		1.53	0.97, 2.39	0.066	1.88	1.14, 3.10	0.013
Hypertention	266
No		—	—		—	—
Yes		1.32	0.70, 2.50	0.4	0.60	0.30, 1.18	0.14
NIHSS Score	266
No stroke symptoms (0)		—	—		—	—
Minor stroke (1-4)		0.38	0.15, 0.99	0.047	0.68	0.25, 1.88	0.5
Moderate stroke (5-15)		1.32	0.55, 3.16	0.5	2.20	0.88, 5.53	0.092
Moderate to severe stroke (16-20)		1.66	0.61, 4.50	0.3	2.64	0.93, 7.48	0.067
Severe stroke (21-42)		4.12	1.56, 10.9	0.004	7.40	2.56, 21.4	<0.001
¹ HR = Hazard Ratio, CI = Confidence Interval

Stacking

t1 <- glm(status_f.u~sex,
          data = stroke2_complete,
          family = binomial) %>% 
  tbl_regression(
    exponentiate=TRUE,
    label=list(sex ~"Gender (unadjusted)")
  )

t2 <- glm(status_f.u~sex+age+dm+hpt,
          data = stroke2_complete,
          family = binomial) %>% 
  tbl_regression(
    include="sex",
    exponentiate=TRUE,
    label=list(sex ~"Gender (adjusted)")
  )

table_stack_ex1 <- tbl_stack(list(t1, t2))
table_stack_ex1

Characteristic	OR¹	95% CI¹	p-value
Gender (unadjusted)
Female	—	—
Male	0.48	0.28, 0.81	0.006
Gender (adjusted)
Female	—	—
Male	0.53	0.30, 0.95	0.032
¹ OR = Odds Ratio, CI = Confidence Interval

Print engine (gtsummary + R Markdown)

The gtsumamry package was developed to complement the gt package from RStudio. The gt package, however, does not support all types of output. As a result, we have provided support for printing gtsummary tables using a variety of engines.

For further information, see the gtsummary + R Markdown vignette.

gtsummary + R Markdown

library(flextable)

## 
## Attaching package: 'flextable'

## The following objects are masked from 'package:gtsummary':
## 
##     as_flextable, continuous_summary

tbl_surv_merge %>% 
  bold_labels() %>% 
  italicize_levels() %>%
  as_flex_table() # if knit to microsoft word

	Univariable				Multivariable
Characteristic	N	HR1	95% CI1	p-value	HR1	95% CI1	p-value
Age	266	1.05	1.03, 1.06	<0.001	1.04	1.02, 1.06	<0.001
Gender	266
Female		—	—		—	—
Male		0.48	0.30, 0.74	0.001	0.60	0.38, 0.96	0.033
Diabetes Mellitus	266
No		—	—		—	—
Yes		1.53	0.97, 2.39	0.066	1.88	1.14, 3.10	0.013
Hypertention	266
No		—	—		—	—
Yes		1.32	0.70, 2.50	0.4	0.60	0.30, 1.18	0.14
NIHSS Score	266
No stroke symptoms (0)		—	—		—	—
Minor stroke (1-4)		0.38	0.15, 0.99	0.047	0.68	0.25, 1.88	0.5
Moderate stroke (5-15)		1.32	0.55, 3.16	0.5	2.20	0.88, 5.53	0.092
Moderate to severe stroke (16-20)		1.66	0.61, 4.50	0.3	2.64	0.93, 7.48	0.067
Severe stroke (21-42)		4.12	1.56, 10.9	0.004	7.40	2.56, 21.4	<0.001
1HR = Hazard Ratio, CI = Confidence Interval

Journal table format

reset_gtsummary_theme()
theme_gtsummary_journal(journal = "lancet")

## Setting theme `The Lancet`

lancet_theme <- tbl_surv_merge %>% 
  bold_labels() %>% 
  italicize_levels() %>%
  as_gt() %>% 
  gt::tab_header("Journal Theme (Lancet)")

lancet_theme

Characteristic	Univariable				Multivariable
Journal Theme (Lancet)
Characteristic	N	HR¹	95% CI¹	p-value	HR¹	95% CI¹	p-value
Age	266	1·05	1.03, 1.06	<0·001	1·04	1.02, 1.06	<0·001
Gender	266
Female		—	—		—	—
Male		0·48	0.30, 0.74	0·001	0·60	0.38, 0.96	0·033
Diabetes Mellitus	266
No		—	—		—	—
Yes		1·53	0.97, 2.39	0·066	1·88	1.14, 3.10	0·013
Hypertention	266
No		—	—		—	—
Yes		1·32	0.70, 2.50	0·4	0·60	0.30, 1.18	0·14
NIHSS Score	266
No stroke symptoms (0)		—	—		—	—
Minor stroke (1-4)		0·38	0.15, 0.99	0·047	0·68	0.25, 1.88	0·5
Moderate stroke (5-15)		1·32	0.55, 3.16	0·5	2·20	0.88, 5.53	0·092
Moderate to severe stroke (16-20)		1·66	0.61, 4.50	0·3	2·64	0.93, 7.48	0·067
Severe stroke (21-42)		4·12	1.56, 10.9	0·004	7·40	2.56, 21.4	<0·001
¹ HR = Hazard Ratio, CI = Confidence Interval

Report statistics in line

Tables are important but sometimes we still need to report a result in-line in a report. This is especially true when explaining the results of regression models for reader’s understanding.

With the inline_text() function, any data reported in the tables using {gtsummary} can be extracted and reported in-line in R markdown

For example:

We want to report hazard ratio for age from table cox_mv

cox_mv

Characteristic	HR¹	95% CI¹	p-value
Age	1·04	1.02, 1.06	<0·001
Gender
Female	—	—
Male	0·60	0.38, 0.96	0·033
Diabetes Mellitus
No	—	—
Yes	1·88	1.14, 3.10	0·013
Hypertention
No	—	—
Yes	0·60	0.30, 1.18	0·14
NIHSS Score
No stroke symptoms (0)	—	—
Minor stroke (1-4)	0·68	0.25, 1.88	0·5
Moderate stroke (5-15)	2·20	0.88, 5.53	0·092
Moderate to severe stroke (16-20)	2·64	0.93, 7.48	0·067
Severe stroke (21-42)	7·40	2.56, 21.4	<0·001
¹ HR = Hazard Ratio, CI = Confidence Interval

For every 1 year increment in age, the hazard of dying from stroke increase by 4%, 1·04 (95% CI 1·02, 1·06; p<0·0001).

More Details

YouTube tutorial by Daniel Sjoberg and Emily Zabor https://www.youtube.com/watch?v=tANo9E1SYJE&t=628s https://www.youtube.com/watch?v=U2S6LbMN42I&t=25s

{gtsummary} website https://www.danieldsjoberg.com/gtsummary/

Conclusion

Tables serves as a tool for communicating discrete data or direct comparison within a report
The {gtsummary} package assists researchers in producing reproducible presentation ready tables

Exploratory Data Analysis using {gtsummary} package